Instructions to use QingyuShi/Muddit_dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use QingyuShi/Muddit_dev with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("QingyuShi/Muddit_dev", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Rename pretrian_70k_llava_mg_llava_vqa_all_gen_lr_1e-4_bs_1024_text_weight_0.6_res_512_.zip to pretrain_70k_llava_mg_llava_vqa_all_gen_lr_1e-4_bs_1024_text_weight_0.6_res_512_.zip
Browse files
pretrian_70k_llava_mg_llava_vqa_all_gen_lr_1e-4_bs_1024_text_weight_0.6_res_512_.zip → pretrain_70k_llava_mg_llava_vqa_all_gen_lr_1e-4_bs_1024_text_weight_0.6_res_512_.zip
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